Sequence Variants with Large Effects on Cardiac Electrophysiology and Disease

ARTICLE

https://doi.org/10.1038/s41467-019-12682-9 OPEN Sequence variants with large effects on cardiac electrophysiology and disease

Kristjan Norland1,12, Gardar Sveinbjornsson1,2,12, Rosa B. Thorolfsdottir1, Olafur B. Davidsson1, Vinicius Tragante1,3, Sridharan Rajamani1, Anna Helgadottir1, Solveig Gretarsdottir1, Jessica van Setten3, Folkert W. Asselbergs3,4,5, Jon Th. Sverrisson6, Sigurdur S. Stephensen7, Gylﬁ Oskarsson7, Emil L. Sigurdsson8,9, Karl Andersen10,11, Ragnar Danielsen10, Gudmundur Thorgeirsson1,10,11, Unnur Thorsteinsdottir1,11, David O. Arnar1,10,11, Patrick Sulem1, Hilma Holm1, Daniel F. Gudbjartsson1,2* & Kari Stefansson 1,11* 1234567890():,;

Features of the QRS complex of the electrocardiogram, reflecting ventricular depolarisation, associate with various physiologic functions and several pathologic conditions. We test 32.5 million variants for association with ten measures of the QRS complex in 12 leads, using 405,732 electrocardiograms from 81,192 Icelanders. We identify 190 associations at 130 loci, the majority of which have not been reported before, including associations with 21 rare or low-frequency coding variants. Assessment of genes expressed in the heart yields an additional 13 rare QRS coding variants at 12 loci. We find 51 unreported associations between the QRS variants and echocardiographic traits and cardiovascular diseases, including atrial fibrillation, complete AV block, heart failure and supraventricular tachycardia. We demonstrate the advantage of in-depth analysis of the QRS complex in conjunction with other cardiovascular phenotypes to enhance our understanding of the genetic basis of myocardial mass, cardiac conduction and disease.

1 deCODE genetics/Amgen Inc., Reykjavik, Iceland. 2 School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland. 3 Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. 4 Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK. 5 Health Data Research UK and Institute of Health Informatics, University College London, London, UK. 6 Department of Internal Medicine, Akureyri Hospital, Eyrarlandsvegur, 600 Akureyri, Iceland. 7 Department of Paediatric Cardiology, Children’s Hospital, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland. 8 Department of Family Medicine, University of Iceland, Reykjavik, Iceland. 9 Department of Development, Primary Health Care of the Capital Area, Reykjavik, Iceland. 10 Division of Cardiology, Internal Medicine Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland. 11 Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland. 12These authors contributed equally: Kristjan Norland, Gardar Sveinbjornsson. *email: [email protected]; [email protected]

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he QRS complex (Fig. 1) of the electrocardiogram (ECG) is demonstrate the advantage of analysing 12 leads of the ECG and Ta summation of electrical activity in the heart during identify rare protein-coding variants in genes that can improve ventricular depolarisation. It represents electrical activation the understanding of risk and progression of heart disease and of the left and right ventricles of the heart, propagated through help direct future studies. the specialised conduction system1, which includes the His bundle, right and left bundle branches and the Purkinje network. Normal ventricular depolarisation is a rapid process activating Results different ventricular myocardial segments in a precise temporal QRS associations. We performed a GWAS on 81,192 indivi- sequence, resulting in a narrow QRS complex with a character- duals, testing 32.5 million common and rare (MAF > 0.01%) istic pattern on the 12-lead ECG1. Deviation from the norm has SNPs and indels for association with 123 QRS complex para- been associated with sudden cardiac arrest (prolonged ventricular meters (Supplementary Fig. 1, Supplementary Tables 1 and 2). activation time)2,3 and mortality in the general population Thevariantswereidentified by whole-genome sequencing (prolonged QRS duration)4 and among individuals free of car- 15,220 Icelanders and imputed into 151,677 long-range phased diovascular disease (low QRS voltage)5. individuals and their relatives16. We adjusted the threshold for Many traits and diseases affect the morphology of the QRS genome-wide significance with a weighted Bonferroni proce- complex, including body habitus, primary conduction abnorm- dure, using as weights the predicted functional impact of alities, hypertrophy and dilatation of the ventricles, myocardial association signals17 (Supplementary Table 3). Sequence var- infarction, pericardial effusion and lung disease6. Measures of the iants at 130 loci (MAF > 0.1%) associate with at least one QRS QRS complex have been used as prognostic indicators and mar- complex parameter. Using conditional analysis, we identified kers of heart disease severity, such as heart failure7, ventricular 60 secondary signals at 32 of the loci, resulting in 190 distinct hypertrophy8 and amyloidosis9. associations (Supplementary Data 1 and 2). We replicated Prior genome-wide association studies (GWAS) of the QRS associations at all 54 reported QRS loci with at least one of the complex were limited to testing QRS duration and voltage criteria 123 QRS parameters (Supplementary Data 3, Supplementary reflecting left ventricular hypertrophy, yielding sequence variants Table 4; P < 0.05/123 = 4.1 × 10−4). – at 58 loci with minor allele frequency (MAF) >4%10 15. Among the 190 variants that associate with QRS parameters, To gain a better understanding of the molecular mechanism of 159 are common (MAF > 5%) with effects ranging in magnitude ventricular conduction, we perform a large GWAS of ten mea- from 0.04 to 0.14 standard deviations (s.d.), 14 are low-frequency sures of the QRS complex in 12 leads from ECGs of 81,192 (1% < MAF ≤ 5%) with effects ranging from 0.11 to 0.35 s.d., and individuals. These ten measures are the Q, R and S wave ampli- 17 are rare (MAF ≤ 1%) with effects ranging from 0.19 to 1.9 s.d. tudes and durations, QRS complex area and duration, ventricular We found genome-wide significant associations with 115 of activation time, and the distance from the peak of the R wave to 123 QRS parameters tested. There were no genome-wide the nadir of the S wave (QRS p-2-p). We analyse each measure for significant associations with eight Q wave amplitude and duration 12 ECG leads: limb leads (I–III), augmented limb leads (aVR, parameters, of which four had relatively few available measure- aVL, aVF) and precordial leads (V1–V6). We also analyse three ments (N < 15,000). Of the 190 variants, 160 associate genome- other parameters: mean QRS duration over 12 leads, the Cornell wide significantly with more than one QRS parameter. The R voltage criterion and the Sokolow-Lyon voltage criterion. In total, amplitude is the QRS measure that captures the most associations we analyse 123 QRS complex parameters and this results in the (N = 96), followed by QRS area (N = 85) and R duration (N = identification of 190 associations between the QRS complex and 76), while the Cornell voltage criterion captures the fewest (N = variants at 130 loci, for which we further assess effects on seven 18) (Supplementary Fig. 2). Some of the variants associate only echocardiographic traits and 22 cardiovascular diseases. We with the R amplitude (N = 16), R duration (N = 9), S amplitude

QRS dur.

VAT aVR aVL R amp. QRS peak-to-peak I V6 V5 V1 V2 V3 V4 + QRS area –– III II aVF

R dur. Q amp. S amp.

Q dur.

S dur.

Fig. 1 ECG wave and the QRS complex. We denote QRS measures used in the analysis with grey lines. We illustrate the different viewpoints of the 12 ECG leads in the upper-right corner. Amp. amplitude, Dur. duration, VAT ventricular activation time

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(N = 9), QRS area (N = 5), QRS p-2-p (N = 4) or ventricular Replication. We assessed the associations of unreported QRS activation time (N = 4). variants in (1) QRS parameters similar to ours from 19,885 ECGs For the R amplitude, we identified most associations with lead of participants in the UK Biobank and (2) a published GWAS14 V1 (N = 47, Fig. 2). Fifteen variants associate only with the R on four QRS measures from up to 73,518 participants. amplitude in lead V1. We identified more than twice as many Using data from the published GWAS on QRS duration and associations for the QRS p-2-p amplitude in lead aVR than in any three voltage criteria14, we tested variants that associated genome- other lead (N = 47). For the QRS area, we identified most wide significantly in the Icelandic material with any of these four associations with lead II (N = 43); with lead V3 for QRS duration parameters. We tested seven variants and all replicated (P < 0.05 (N = 39); with lead V5 for R duration (N = 33), S duration (N = and the same direction of effects, Supplementary Data 6). In the 33) and S amplitude (N = 31); and with lead V6 for ventricular smaller UK Biobank data, we tested variants that we expected to activation time (N = 28), Q amplitude (N = 24) and Q duration replicate (over 99% power). Of 13 variants tested, all had the same (N = 9). Across all QRS measures, we observed the fewest direction of effects in both datasets and 11 replicated (P < 0.05 associations with leads III and aVL. and the same direction of effects, Supplementary Data 7). The genes harbouring the missense and loss-of-function variant associations likely encode proteins that play roles in the Associations with echocardiographic traits. Using echocardio- biology of the QRS complex. Variants annotated as coding/splice grams of 21,275 Icelanders, we tested the QRS variants for asso- represent 47 of the 190 QRS associations, of which 28 are at ciation with 7 echocardiographic left ventricular measurements unreported QRS loci—14 rare and 7 low frequency (Supplemen- (Supplementary Table 5, Supplementary Data 8). We observed 23 tary Data 1 and 2). Loss-of-function variants in HMCN2 and − associations with 18 variants (Table 1,5%FDR,P ≤ 7.1 × 10 4), SH3BGR associate most significantly with the QRS p-2-p most with the left ventricular end-diastolic diameter (LVEDD). The amplitude, in MYBPC3 and TTN with R wave duration and splice-acceptor site mutation c.927-2 A > G in MYBPC3,knownto SCN5A with QRS duration. MYBPC3, TTN and SCN5A are cause familial hypertrophic cardiomyopathy19,associateswith established cardiac disease genes, but SH3BGR and HMCN2 are − interventricular septum thickness (β = 1.2 s.d., P = 1.5 × 10 37), left not. Of the missense variants, 20 associate most significantly with ventricular posterior wall thickness and LVEDD. The missense the R amplitude (in ADAMTS7, ALPK3, BAG3, CASP7, variant p.Cys151Arg in BAG3, known to associate with heart failure CCDC141, CILP, DERL3, FHOD3, FLNC, MYH6, MYH7, due to dilated cardiomyopathy20, associates with LVEDD (β = MYH7B, MYO18B, NACA, PLEC, RYR2, STAB1 and TTN), 4 − −0.10 s.d., P = 1.8 × 10 13). We discovered ten unreported asso- with the QRS area (in ALDH1A2, RBM20 and TTN) and 4 with ciations, including an association with rs4794562[T] intronic to the QRS p-2-p amplitude (in CAND2, HMCN2, SENP2 and HLF, encoding a circadian PAR bZip transcription factor21,and STON1-GTF2A1L). Twelve variants associate most significantly − LVEDD (β = −0.06 s.d., P = 5.3 × 10 6). with other QRS parameters (in ADAMTS6, ADPRHL1, C17orf58, CCDC141, CFAP46, KLHL38, LAMA3, MAPT, PLCE1, SCN10A, SYNPO2L and TTN). Associations with cardiovascular diseases. The 190 QRS variants We tested the 190 QRS variants for association with the are a priori more likely to associate with cardiovascular diseases ventricular conduction disorders, left anterior/posterior fasci- than random sequence variants, and thus we tested them for cular block (LAFB/LPFB) and left/right bundle branch block association with 22 cardiovascular diseases in Icelandic datasets, (LBBB/RBBB). Using a 5% false discovery rate (FDR, P ≤ 0.003), including cardiac arrhythmias, cardiomyopathy and coronary we observed 46 associations with 37 variants (Supplementary artery disease (CAD). For 18 of the diseases, we also had data Data 4). Three variants associate genome-wide significantly from the UK Biobank22 and performed a meta-analysis (Sup- with LAFB, including the missense variant p.Leu294Arg plementary Table 6, Supplementary Data 9). (MAF = 4.4%) in ADPRHL1 (OR = 1.37, P = 4.8 × 10−8), cod- Two rare QRS variants associate with several cardiovascular ing for a cardiac-restricted protein essential for heart chamber dysfunctions. C.927-2A>G in MYBPC3 (MAF = 0.18%) associ- outgrowth18. The stop-gain variant p.Ser699Ter in HMCN2 ates with cardiomyopathy, heart failure, ventricular tachycardia (MAF = 2.0%) associates with a high risk of LPFB (OR = 2.31, and atrial fibrillation (AF), and p.Arg721Trp (MAF = 0.34%) in P = 5.2 × 10−4). MYH6 with sick sinus syndrome (SSS), AF and coarctation of the To assess the genetic relationship between ventricular aorta. We have previously reported the effects of these variants on depolarisation and other electrical functions of the cardiac risk and progression of heart diseases, as well as two other QRS conduction system, we tested the genetic correlation between variants at PLEC and PALMD19,23–26. Excluding these four different ECG measures. The genetic correlation (Methods) variants, we observed 91 associations with 57 of the remaining between mean QRS duration and mean PR interval duration is QRS variants (5% FDR, P ≤ 1.0 × 10−3). Most of the associations 0.14 (95% CI: 0.06–0.21) and between mean QRS duration and are with AF (N = 30), essential hypertension (N = 11), CAD mean QT interval duration is 0.31 (95% CI: 0.23-0.40). We also (N = 10) and myocardial infarction (N = 10), of which many tested the 190 QRS variants for association with ECG measures have been reported27–34. We also identified reported associations reflecting atrial and atrioventricular conduction (P–Qcompo- with dilated cardiomyopathy and heart failure20, SSS and nent of the ECG), and ventricular repolarisation (ST–T presence of pacemaker10. component) (Supplementary Fig. 3, Supplementary Data 5). We found 41 unreported associations with 32 variants and 16 Using a 5% FDR (P ≤ 0.016), 177 of the variants associate with a cardiovascular traits (Table 2). Notably, the intronic variant non-QRS measure of the ECG. Of those, 16 have more rs11166990[A] in PXN, which associates with the R amplitude in significant effects on non-QRS components: the PR interval lead V1, associates with reduced risk of AF (MAF = 1.3%, OR = (at CAV1, OBFC1, SCN5A, SCN10A and TBX3), T amplitude (at 0.82, P = 4.0 × 10−8). Paxillin, encoded by PXN, is expressed at KLF12, LMF1, MYBPC3 and RNF207), P amplitude (at focal adhesions of non-striated cells and costameres of striated SYNPO2L and MYH6) and ST duration (at MYH7B and muscle cells35. Rs11166990[A] upstream of PTK2 (coding for focal RNF207). The pattern of magnitude and direction of effects adhesion kinase), which also associates with the R amplitude in associated with different ECG parameters is highly variable lead V1, associates with hypertension (MAF = 48%, OR = 0.97, P between variants. = 1.1 × 10−6), aortic valve stenosis (OR = 0.92, P = 2.1 × 10−4),

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a Q amplitude b Q duration c Ventricular activation time 15 8 4 15 7 4 3 6 3 10 4 2 7 4 3 2 5 3 1 2 1 2 1 1 25 0 0 8 0 0 25 0 0 V1 V1 V1 V2 V2 V2 V3 V3 V3 V4 V4 V4 V5 V5 V5 V6 V6 V6 I I I II II II III III III aVF aVF aVF aVL aVL aVL aVR aVR aVR

d R amplitude e S amplitude 15 15 15 15

10 10 8 7 5 5 4 5 43 5 3 2 1 2 1 40 0 0 30 20 10 0 0 V1 V1 V2 V2 V3 V3 V4 V4 V5 V5 V6 V6 I I II II III III aVF aVF aVL aVL aVR aVR

f R duration g S duration 5 5 4 4 4 4 3 3 2 2 2 2 1 1 30 0 0 30 0 0 V1 V1 V2 V2 V3 V3 V4 V4 V5 V5 V6 V6 I I II II III III aVF aVF aVL aVL aVR aVR

h QRS area i QRS duration 6 5 6 4 4 4 3 4 3 2 2 2 2 1 1 40 0 0 40 0 0 V1 V1 V2 V2 V3 V3 V4 V4 V5 V5 V6 V6 I I II II III III aVF aVF aVL aVL aVR aVR

j QRS peak-to-peak 12 10 5 4 5 3 2 1 40 0 0 V1 V2 V3 V4 V5 V6 I II III aVF aVL aVR

Fig. 2 Intersection plots of associations for the QRS measures. In each panel, the left bar plot shows how many of the 190 QRS variants associate (P <5× 10−8) with each lead. The top bar plot shows the sizes of intersecting sets of associations for the leads denoted with (connected) black dots. a Q amplitude; b Q duration; c Ventricular activation time; d R amplitude; e S amplitude; f R duration; g S duration; h QRS area; i QRS duration; j QRS peak-to- peak

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Table 1 Associations between the QRS variants and echocardiographic traits

Trait Variant Chr. Pos. (hg38) EA/OA EAF (%) Locus Annotation Top QRS parameter Effect (s.d.) P ARD rs11874 17 46,939,827 A/G 12.9 GOSR2 3′ UTR S dur., V4 0.11 8.0 × 10−9 ARD rs7543039 1 99,584,092 C/T 48.8 PALMD Intergenic S amp., V4 −0.064 1.5 × 10−8 ARD rs2687938 13 50,180,110 T/C 45.6 DLEU1 Intergenic QRS area, V4 0.057 4.6 × 10−7 ARD rs335196 5 123,184,837 A/G 43 PRDM6 Intronic QRS area, I −0.055 1.8 × 10−6 ARD rs34766086* 2 40,512,632 T/G 5.67 SLC8A1 Upstream R dur., V5 −0.085 7.1 × 10−4 EF rs2234962 10 119,670,121 C/T 24.2 BAG3 Missense R amp., V1 0.051 1.7 × 10−4 EF rs72692220* 1 50,974,791 A/G 3.15 CDKN2C Downstream Mean QRS dur. −0.13 1.8 × 10−4 EF rs35006907 8 124,847,575 A/C 35.8 ZNF572 Intergenic R amp., V1 0.042 6.0 × 10−4 LVEDD rs2234962 10 119,670,121 C/T 24.2 BAG3 Missense R amp., V1 −0.099 1.8 × 10−13 LVEDD rs397516082 11 47,346,372 C/T 0.175 MYBPC3 Splice acceptor R dur., V4 −0.57 2.3 × 10−9 LVEDD rs35006907 8 124,847,575 A/C 35.8 ZNF572 Intergenic R amp., V1 −0.058 9.7 × 10−7 LVEDD rs4794562* 17 55,302,450 T/C 25.7 HLF Intronic QRS dur., V3 −0.06 5.3 × 10−6 LVEDD rs17305149* 3 14,235,797 G/A 18.5 LSM3 Intergenic R dur., V5 0.064 1.8 × 10−5 LVEDD rs72692220* 1 50,974,791 A/G 3.15 CDKN2C Downstream Mean QRS dur. 0.14 4.0 × 10−5 LVEDD rs16866538 2 178,795,185 A/G 4.93 TTN Missense S amp., V1 −0.098 2.3 × 10−4 LVEDD rs143158900* 10 112,746,619 CA/C 17.6 VTI1A Intronic QRS p-2-p, I 0.055 2.6 × 10−4 LVEDD rs147436240 6 36,680,287 C/CGCGT 17.2 CDKN1A Intronic Mean QRS dur. −0.055 3.9 × 10−4 LVEDD rs9909004* 17 66,310,015 C/T 38.6 PRKCA Intronic QRS dur., V3 0.041 5.5 × 10−4 LVESD rs1461990* 8 107,075,400 G/C 47.5 ANGPT1 Intergenic Q amp., V6 0.07 5.8 × 10−4 LVPWT rs397516082 11 47,346,372 C/T 0.175 MYBPC3 Splice acceptor R dur., V4 0.62 4.9 × 10−11 IVST rs397516082 11 47,346,372 C/T 0.175 MYBPC3 Splice acceptor R dur., V4 1.2 1.5 × 10−37 IVST rs4284441* 12 114,924,167 T/C 36.5 TBX3 Intergenic QRS area, V6 0.047 7.2 × 10−5 IVST rs7301677* 12 114,943,342 T/C 34.8 TBX3 Intergenic VAT, V6 −0.041 5.7 × 10−4

We only show results for 5% FDR, P ≤ 7.1 × 10−4. We denote unreported associations with a star. EA effect allele, OA other allele, EAF effect allele frequency, s.d. standard deviation, Dur. duration, Amp. amplitude, ARD aortic root diameter (N = 19,513), EF ejection fraction (N = 17,109), LVEDD left ventricular end-diastolic dimension (N = 18,487), LVESD left ventricular end-systolic dimension (N = 5704), LVPWT left ventricular posterior wall thickness (N = 18,626), IVST interventricular septum thickness (N = 18,775)

and is known to associate with reduced risk of AF. The rare in a subset of our data containing 236,896 ECGs in sinus rhythm missense variant p.Ala2397Val in FLNC (MAF = 0.13%), which of 57,399 individuals without AF. Most variants still had the associates with a larger R amplitude in lead V1, reduces the risk of strongest effects on the QRS complex and non-significant effects myocardial infarction (OR = 0.42, P = 1.3 × 10−4) and delays its on the P amplitude (5% FDR), indicating that the QRS effect is onset (β = 0.78 s.d., P = 1.1 × 10−4), with greater protection not a consequence of the arrhythmia (Supplementary Fig. 4). against myocardial infarction in individuals younger than 76 years (OR = 0.21, P = 3.8 × 10−6). Variants in FLNC are known to Parent-of-origin effects. We tested the 190 QRS variants for a cause cardiomyopathy, and we recently reported the frameshift difference in effects under maternal and paternal models of variant p.Phe1626Serfs*40 in FLNC36 with opposite direction of transmission (Supplementary Table 8)37. Two variants have more effects on the R amplitude in lead V1 and myocardial infarction to than three times greater effect when inherited paternally than those of p.Ala2397Val (Supplementary Table 7). maternally (Table 3, P < 0.05/190 = 2.6 × 10−4). One is We found four unreported associations with supraventricular rs11761424[A] (MAF = 35%) intronic to DGKB (encoding a tachycardia (SVT), represented by rs75013985[G] intronic to − diacylglycerol kinase), which associates most significantly with R KCND3 (AF = 2.2%, OR = 1.61, P = 2.7 × 10 9); rs629234[T] − wave duration in lead V4 (β = 0.068 s.d., β = 0.018 s.d., upstream of PRRX1 (MAF = 47.9%, OR = 1.11, P = 8.6 × 10 6), pat mat P = 4.1 × 10−5). Sequence variants near DGKB are known to which also associates with AF and CAD; rs12144451[C] in het − associate with type 2 diabetes38 and AF27 but do not correlate CASQ2 (MAF = 43.1%, OR = 0.92, P = 4.4 × 10 4), which also with rs11761424 (r2 < 0.01). The other is rs116904997[A] associates with AF; and the missense variant p.Arg935Trp in = CCDC141 (MAF = 11.7%, OR = 1.14, P = 7.9 × 10−4). We found (MAF 1.3%) intronic to PXN (encoding paxillin), which associates most significantly with the R amplitude in lead V1 three associations with complete AV block (CAVB), represented β = β = = −4 by p.Glu382Asp in CCDC141 (MAF = 15.3%, OR = 1.29, P = ( pat 0.28 s.d., mat 0.073 s.d., Phet 1.6 × 10 ). Paxillin is a −7 cytoskeletal protein involved in actin-membrane attachment at 3.6 × 10 ), which also associates with pacemaker insertion 35 (OR = 1.12, P = 5.9 × 10−5) and SSS (OR = 1.13, P = 2.1 × 10−4); sites of focal adhesion . In the Genotype-Tissue Expression (GTEx) dataset39, rs116904997[A] associates more significantly rs4794562[T] intronic to HLF (MAF = 25.7%, OR = 0.86, P = − than any other variant with the expression of PXN in the left 7.8 × 10 4), which also associates with heart failure (OR = 0.95, − − ventricle of the heart (Supplementary Fig. 5, P = 3.0 × 10 9). P = 6.7 × 10 5); and rs17226667[A] near CEP85L (MAF = 45.8%, OR = 1.13, P = 8.1 × 10−4). We found three unreported associa- These loci are not known to be imprinted. tions with dilated cardiomyopathy, represented by the missense variant p.Val77Ala in CAND2 (MAF = 32.7%, OR = 0.82, P = Pathway analysis. We applied the data-driven expression-priori- − 3.9 × 10 5), rs35006907[A] near ZNF572 (MAF = 35.8%, OR = tised integration for complex traits (DEPICT, Methods)40 to − 0.82, P = 7.0 × 10 5), and rs10838681[A] upstream of NR1H3 search for tissues and cell types in which our variants are more − (MAF = 31.8%, OR = 1.19, P = 3.1 × 10 4). likely to be expressed. Enrichment testing of expression in 209 Some of the QRS variants that associate with AF have effects tissues and cell types pointed out cardiovascular tissue (atria, on non-QRS components of the ECG, mainly the P amplitude ventricles and arteries) as well as the myometrium and muscles and the PR interval, measuring atrial and atrioventricular nodal (1% FDR, Supplementary Data 10). Furthermore, DEPICT iden- conduction (Supplementary Fig. 3). We tested these QRS variants tified 495 enriched gene sets out of 14,461 reconstituted gene sets

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Trait Chr. Pos. (hg38) EA/OA Locus Comb. OR (95% CI) Comb. PPhet Ice OR Ice P Ice EAF (%) UKB OR UKB P UKB EAF (%) AVS 8 140,653,873 A/G PTK2 0.92 (0.87, 0.96) 2.1 × 10−4 0.29 0.89 7.0 × 10−4 48 0.94 0.066 49.2 AF 12 120,230,731 A/G PXN 0.82 (0.76, 0.88) 4.0 × 10−8 0.86 0.83 0.01 1.28 0.81 1.2 × 10−6 2.25

AUECOMMUNICATIONS NATURE AF 6 22,570,808 G/A HDGFL1 1.04 (1.02, 1.07) 1.2 × 10−4 0.97 1.04 0.023 23.1 1.04 0.002 27.3 AF 6 117,168,041 T/G VGLL2 1.04 (1.02, 1.06) 1.8 × 10−4 0.15 1.02 0.29 38.6 1.05 1.1 × 10−4 38.9 AF 17 67,991,933 T/C C17orf58* 1.04 (1.02, 1.06) 2.2 × 10−4 0.45 1.05 0.0046 24.9 1.03 0.013 27.8 AF 2 238,142,909 T/C KLHL30 0.95 (0.92, 0.98) 5.7 × 10−4 0.66 0.95 0.082 9.89 0.94 0.0027 11.2 AF 5 154,492,281 G/C HAND1 1.03 (1.01, 1.05) 7.9 × 10−4 0.46 1.02 0.15 35.9 1.04 0.0018 36.7 CAVB 2 178,905,448 A/C CCDC141* 1.29 (1.17, 1.42) 3.6 × 10−7 0.56 1.26 3.3 × 10−4 15.3 1.33 2.6 × 10−4 13.8 CAVB 17 55,302,450 T/C HLF 0.86 (0.79, 0.94) 7.8 × 10−4 0.14 0.91 0.094 25.7 0.8 0.0011 26.7 CAVB 6 118,400,465 A/G CEP85L 1.13 (1.05, 1.22) 8.1 × 10−4 0.49 1.11 0.031 45.8 1.17 0.0079 55.3 CMCS 10 33,233,881 G/A NRP1 1.30 (1.14, 1.50) 1.7 × 10−4 0.66 1.27 0.016 6.29 1.35 0.0035 7.8 SPTBN1 −4

21)1:83 tp:/o.r/013/447091629|www.nature.com/naturecommunications | https://doi.org/10.1038/s41467-019-12682-9 | 10:4803 (2019) | CMCS 2 54,665,663 G/AS 0.84 (0.77, 0.92) 2.0 × 10 0.45 0.87 0.023 26.5 0.81 0.0024 27.1 CAD 12 114,924,167 T/C TBX3 1.03 (1.02, 1.05) 2.8 × 10−5 0.27 1.04 6.5 × 10−4 36.5 1.03 0.0078 39 CAD 1 170,662,622 T/C PRRX1 1.03 (1.01, 1.04) 6.9 × 10−4 0.8 1.03 0.026 47.9 1.02 0.01 45.7 DCM 3 12,807,323 T/C CAND2* 0.82 (0.74, 0.90) 3.9 × 10−5 0.87 0.81 0.013 32.7 0.82 0.001 33.7 DCM 8 124,847,575 A/C ZNF572 0.82 (0.74, 0.90) 7.0 × 10−5 0.75 0.84 0.035 35.8 0.81 7.2 × 10−4 31 DCM 11 47,253,513 A/G NR1H3 1.19 (1.08, 1.31) 3.1 × 10−4 0.99 1.19 0.03 31.8 1.19 0.0041 27.4 HF 17 55,302,450 T/C HLF 0.95 (0.92, 0.97) 6.7 × 10−5 0.57 0.95 0.005 25.7 0.94 0.0039 26.7 HF 4 113,508,251 T/C CAMK2D 1.04 (1.02, 1.07) 0.001 0.45 1.04 0.028 27 1.06 0.01 24.7 HT 8 140,653,873 A/G PTK2 0.97 (0.96, 0.98) 1.1 × 10−6 0.47 0.97 0.0028 48 0.98 9.4 × 10−5 49.2 HT 12 56,720,316 A/G NACA* 1.02 (1.01, 1.04) 1.7 × 10−4 0.95 1.02 0.075 22.7 1.02 9.4 × 10−4 27.3

HT 11 47,253,513 A/G NR1H3 0.98 (0.97, 0.99) 2.7 × 10−4 0.47 0.99 0.25 31.8 0.98 4.1 × 10−4 27.4 https://doi.org/10.1038/s41467-019-12682-9 | COMMUNICATIONS NATURE HT 2 174,603,041 T/C WIPF1 1.02 (1.01, 1.04) 4.6 × 10−4 0.12 1 0.78 22.3 1.03 1.3 × 10−4 20.6 HT 8 9,152,396 A/G PPP1R3B 1.02 (1.01, 1.03) 6.3 × 10−4 0.89 1.02 0.13 26.8 1.02 0.0021 31.3 HT 8 11,932,112 T/G DEFB136 0.96 (0.94, 0.98) 6.4 × 10−4 1 0.96 6.4 × 10−4 32.5 —— — HCM 8 124,847,575 A/C ZNF572 1.31 (1.14, 1.52) 2.1 × 10−4 0.23 1.24 0.018 35.8 1.5 0.002 31 IS 11 19,986,122 T/A NAV2 1.07 (1.03, 1.11) 7.9 × 10−4 0.7 1.06 0.021 24.5 1.08 0.014 23.4 MI 7 128,856,555 T/C FLNC* 0.42 (0.27, 0.66) 1.3 × 10−4 1 0.42 1.3 × 10−4 0.134 —— — MI 2 36,454,050 A/G CRIM1 1.03 (1.01, 1.05) 4.1 × 10−4 0.28 1.02 0.12 37.4 1.04 8.0 × 10−4 40.8 MI 18 36,652,647 T/C FHOD3* 0.97 (0.95, 0.99) 0.001 0.84 0.96 0.019 30.1 0.97 0.021 28.6 PMI 2 178,905,448 A/C CCDC141* 1.12 (1.06, 1.18) 5.9 × 10−5 0.91 1.12 0.0018 15.3 1.11 0.011 13.8 PMI 1 6,212,076 G/GA RNF207 0.91 (0.86, 0.96) 6.2 × 10−4 1 0.91 6.2 × 10−4 36.5 —— — PMI 4 113,508,251 T/C CAMK2D 1.08 (1.03, 1.12) 8.7 × 10−4 0.08 1.11 2.5 × 10−4 27 1.03 0.38 24.7 PDA 5 65,470,971 A/G ADAMTS6* 4.88 (1.90, 12.55) 0.001 1 4.88 0.001 0.245 —— — PMC 12 114,943,342 T/C TBX3 0.84 (0.75, 0.93) 9.7 × 10−4 1 0.84 9.7 × 10−4 34.8 —— — SSS 4 113,508,251 T/C CAMK2D 1.11 (1.05, 1.18) 1.2 × 10−4 0.76 1.11 4.7 × 10−4 27 1.14 0.1 24.7 SSS 2 178,905,448 A/C CCDC141* 1.13 (1.06, 1.21) 2.1 × 10−4 0.22 1.15 9.6 × 10−5 15.3 1.01 0.89 13.8 SVT 1 111,987,808 G/A KCND3 1.61 (1.38, 1.88) 2.7 × 10−9 0.79 1.57 1.7 × 10−4 2.21 1.64 3.7 × 10−6 1.38 SVT 1 170,662,622 T/C PRRX1 1.11 (1.06, 1.17) 8.6 × 10−6 0.76 1.12 0.0047 47.9 1.11 5.6 × 10−4 45.7 SVT 1 115,749,108 C/T CASQ2 0.92 (0.88, 0.96) 4.4 × 10−4 0.36 0.89 0.0052 43.1 0.93 0.02 44.6 SVT 2 178,856,319 A/G CCDC141* 1.14 (1.06, 1.23) 7.9 × 10−4 0.36 1.09 0.18 11.7 1.17 0.0013 8.53

We show results for 5% FDR, P ≤ 0.001. We denote coding variants with a star. EA effect allele, OA other allele; Phet P value for heterogeneity in the effect estimates between the Icelandic and UK Biobank data, EAF effect allele frequency, AVS aortic valve stenosis, AF atrial ﬁbrillation, CAVB complete atrioventricular block, CMCS congenital malformations of cardiac septa, CAD coronary artery disease, DCM dilated cardiomyopathy, HF heart failure, HT hypertension, HCM hypertrophic cardiomyopathy, IS ischaemic stroke, MI myocardial infarction, PMI pacemaker insertion, PDA patent ductus arteriosus, PMC perimyocarditis, SSS sick sinus syndrome, SVT supraventricular tachycardia NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-12682-9 ARTICLE

Table 3 QRS variants with parent-of-origin effects

Top QRS Variant Chr. Pos. (hg38) EA/ EAF (%) Locus Pat. Pat. P Mat. Mat. PPhet parameter OA effect (s.d.) effect (s.d.) R dur., V4 rs11761424 7 14,414,730 A/G 35 DGKB 0.068 6.5 × 10−15 0.018 0.034 4.1 × 10−5 R amp., V1 rs116904997 12 120,230,731 A/G 1.28 PXN 0.28 7.3 × 10−13 0.073 0.062 1.6 × 10−4

−4 We show results for Phet < 0.05/190 = 2.6 × 10 . EA effect allele, OA other allele, EAF effect allele frequency, Pat. paternal, s.d. standard deviation, Mat. maternal, Phet P value for heterogeneity in the effect estimates between the paternal and maternal models, Dur. duration, Amp. amplitude

(1% FDR, Supplementary Data 11). After clustering gene sets by Many of the 123 parameters tested are correlated, and most similarity, the most significant gene sets related to abnormal variants associate with more than one. However, some variants myocardium layer morphology, pericardial effusion, abnormal associate with only one QRS measure, some with only one lead. heart morphology and the cell-substrate adherens junction. Many The R amplitude is the QRS measure that captures most QRS genes near unreported QRS variants were part of gene sets linked associations, about half of them. The R wave in lead V1 captures to the adherens junction, including VCL, SMTN, PXN, FHL2, SVIL half of the R amplitude associations, and 15 variants associate and ANKRD1. We also used DEPICT to point out genes within only with the R amplitude in lead V1. Under normal conditions, each associated locus based on functional similarity to genes within the R wave is small in lead V1 but gets progressively larger in the other associated loci (5% FDR, Supplementary Data 12). precordial leads, usually reaching maximum amplitude in lead Of the 190 QRS variants, 20 are intronic or coding in genes with V5. The small R wave in lead V1 represents the initial part of higher expression in heart muscle than other tissue types in the ventricular depolarisation. Some conditions may cause an Human Protein Atlas (total 201 genes)41.Variantsinsomeofthese abnormally large R wave in lead V1, including right bundle genes have been described by OMIM as causing autosomal branch block, type A Wolf–Parkinson–White Syndrome, pos- dominant heart conditions (MYBPC3, MYH6, MYH7, CTNNA3, terior myocardial infarction, right ventricular hypertrophy and RBM20, RYR2, SCN5A) or associated in GWAS with heart diseases acute right ventricular dilatation48. The QRS area captures the (CASQ2, MYH7B, MYO18B, SYNPO2L, TBX5, TRIM63)27,42,43, second highest number of associations. Although the QRS area resting heart rate (CCDC141, FHOD3)44 or non-QRS ECG has not been used in a GWAS before, it improves identification of parameters (ALPK3, RNF207)45,46. Other genes have not been left ventricular hypertrophy over standard voltage criteria49. The implicated in heart diseases (ADPRHL1, KLHL38, SH3BGR). genetic influence on various aspects of the cardiac conduction Given both the QRS association and the heart muscle expression, system is both tightly linked and complex, as the majority of QRS variants in these genes could substantially affect cardiac function. variants associates also with ECG measures reflecting atrial and Using the prior information on expression of genes in the atrioventricular conduction, and ventricular repolarisation, but heart, we searched for additional rare (MAF ≤ 1%) coding with a variable magnitude and direction of effects. variants that associate with the QRS complex in these 201 genes. Before our study, only two low-frequency (1% ≤ MAF ≤ 5%) Thirteen additional variants associate with one or more QRS and no rare variants had been shown to associate with the QRS parameters (Supplementary Data 13, P < 0.05/(number of coding complex in a GWAS14. We found 33 unreported large-effect variants tested) = 2.4 × 10−5). We found four missense variants at associations with rare or low-frequency coding variants, including unreported QRS loci: C10orf71, FBXO40, GRM1 and HCN4.We 13 associations identified by using a priori expression informa- note that HCN4 is a known bradycardia gene47. We identified one tion. This includes associations with a missense variant in frameshift and three missense QRS variants where we had ADPRHL1 that also associates genome-wide significantly with left identified non-coding QRS variants previously in our study: in anterior fascicular block, and a stop-gain variant in HMCN2 that CTNNA3, NKX2-5, RNF207 and TBX5. We recently reported the also associates with a high risk of left posterior fascicular block. missense variant p.Phe145Leu in NKX2-5 as causing cardiomyo- The rare missense variant p.Ala2397Val in FLNC associates with pathy and arrhythmias36. a larger R amplitude in lead V1 and reduced risk of myocardial infarction, with opposite direction of effects to those of p. Phe1626Serfs*40, a reported frameshift variant that causes car- Discussion diomyopathy, thus suggesting an opposite functional effect of p. We used whole-genome sequence data to perform a GWAS on Ala2397Val36. FLNC encodes filamin C, a large actin-crosslinking the QRS complex of the 12-lead ECG in 81,192 Icelanders and protein expressed in striated muscles. Filamin C anchors major identified 130 QRS loci. We found 190 distinct QRS variants, of protein complexes at the sarcolemma, Z-discs and intercalated which 106 are at 86 loci that have not been reported for QRS discs in cardiomyocytes to the actin cytoskeleton and provides a before, and assessed their effects on seven echocardiographic scaffold for a variety of cytoplasmic signalling proteins50,51. traits and 22 cardiovascular diseases. We identified 91 associations for 57 of the QRS variants with a We demonstrate an advantage of performing an in-depth spectrum of cardiovascular diseases, including arrhythmias and examination of the QRS complex when studying the genetic conduction disorders, hypertension, ischemic disease, cardio- influence on ventricular depolarisation, myocardial mass and myopathies, and valve diseases. We found 41 unreported asso- associated cardiac disease. Our approach yielded 130 QRS loci, ciations, including four with supraventricular tachycardia (SVT), compared to 52 loci identified in a recent GWAS of the QRS one of which is with a low-frequency variant located near complex14 of a similar size (up to 73,518 individuals) that KCND3, a potassium channel gene that has been implicated in assessed only the QRS duration and three voltage criteria, mea- Brugada syndrome52 and AF27, and is the first genome-wide sures commonly used clinically. If we had exclusively tested these significant SVT association. same four measures, we would have identified associations at only We found an association with an intronic variant in PXN, 54 loci. Thus, additional measures of the QRS complex (the QRS encoding paxillin, which has not been associated with AF before. area, ventricular activation time and distinct measures of the Q, R Paxillin is expressed at focal adhesions of non-striated cells and and S waves) yielded 76 additional loci. costameres of striated muscle cells35, and regulates cardiac

NATURE COMMUNICATIONS | (2019) 10:4803 | https://doi.org/10.1038/s41467-019-12682-9 | www.nature.com/naturecommunications 7 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-12682-9 contractility in the zebrafish53. The PXN variant, as well as a TruSeq methodology to a mean depth of 35× (s.d. 8X) and imputed into 151,677 variant intronic to DGKB, associates primarily with the QRS chip-typed individuals and their first- and second-degree relatives16,56. In the UK fi complex when the allele is inherited paternally. These loci are not Biobank, genotyping was performed using a custom-made Af metrix chip (UK BiLEVE Axiom)57 in the first 50,000 participants and Affimetrix UK Biobank known to be imprinted, although paxillin has been shown to Axiom array in the remaining participants58 (95% of the signals were on both regulate expression, not imprinting, of the imprinted genes IGF2 chips). Wellcome Trust Centre for Human Genetics performed the imputation and H19 through long-range chromosomal interactions between using a combination of 1000 Genomes phase 3 (ref. 59), UK10K60 and HRC61 the IGF2 and H19 promoters and a shared distal enhancer54. reference panels, for up to 92.5 million SNPs. A common variant in HLF associates with a decreased risk of heart failure, a lower risk of CAVB and RBBB, a smaller ventricle Statistical analysis. We adjusted quantitative traits for sex and age at measure- ment, transformed them into a standard normal distribution using a rank-based and a shorter QRS duration. HLF encodes a member of the inverse normal transformation, and used a linear mixed model implemented by proline acidic-rich (PAR) protein family, a subset of the bZIP BOLT-LMM62 to test them for association with genotypes. We used a logistic transcription factors that accumulate with robust circadian regression model to test binary traits for association with genotypes. For the rhythms in tissues with high amplitudes of clock gene expression. deCODE data, we adjusted for sex, county of birth, current age or age at death (first- and second-order terms included), blood sample availability for the indivi- The knockout of HLF along with two other PAR bZip tran- dual, and an indicator function for the overlap of the lifetime of the individual with scription factors has been shown to lead to cardiac hypertrophy the timespan of phenotype collection. For the UK Biobank data, we used 40 and left ventricular dysfunction in mice55. principal components to adjust for population stratification and adjusted for age In summary, our results demonstrate the advantage of ana- and sex in the logistic regression model. The UK Biobank study included only white British individuals. In each study, we used LD score regression63 to account lysing the QRS complex in a detailed manner. The use of whole- for inflation in test statistics due to cryptic relatedness and stratification. To genome sequencing facilitates the discovery of associations with combine the deCODE and UK Biobank results, we used a fixed-effect inverse protein-coding variants that implicate particular genes in the variance method based on effect estimates and standard errors64. We used a biology of ventricular depolarisation and cardiac pathology. The likelihood-ratio test to compute all P values. In conditional analyses, we tested 2 findings provide new insights into the complex genetics of cardiac variants within ±1 Mb from the top variant and with correlation r < 0.05 with the top variant, and required the variants to be genome-wide significant after cor- electrophysiology and will help direct future functional studies of recting for the top variant in a regression model. cardiovascular diseases. Genetic correlation. We estimated the genetic correlation between pairs of traits 65 Methods using LD score regression (v.1.0.0) and pre-computed LD scores for European populations (downloaded from https://data.broadinstitute.org/alkesgroup/LDSCORE/ Icelandic data — . The ECGs were performed between 1998 and 2015 at Landspitali eur_w_ld_chr.tar.bz2). The National University Hospital, the sole tertiary care hospital in Iceland. They were done in both inpatient and outpatient setting, using the Philips PageWriter Trim III, Philips PageWriter 200, Philips PageWriter 50 and Philips PageWriter 70 Depict. We used DEPICT40 to (1) prioritise candidate causal genes at associated cardiographs, and stored in the Philips TraceMasterVue ECG Management System. loci, (2) highlight enriched pathways, and (3) identify tissues/cell types where genes We analysed ten measures of the QRS complex from 405,732 ECGs of 81,192 at associated loci are highly expressed. DEPICT uses gene expression data derived individuals. These ten measures were the Q, R and S wave amplitudes and durations; from a panel of 77,840 mRNA expression arrays together with 14,461 existing gene QRS complex area and duration; ventricular activation time (VAT, time between the sets based on molecular pathways derived from experimentally verified onset of the Q wave and the peak of the R wave); and the distance from the peak of protein–protein interactions66, genotype–phenotype relationships from the Mouse the R wave to the nadir of the S wave (QRS peak-to-peak amplitude). We analysed Genetics Initiative67, Reactome pathways68, KEGG pathways69, and Gene Ontol- each measure for 12 ECG leads: limb leads (I–III), augmented limb leads (aVR, aVL, ogy (GO) terms70. DEPICT reconstitutes these 14,461 gene sets by calculating for aVF) and precordial leads (V1–V6). In addition, we analysed mean QRS duration each gene the probability of membership in each gene set, based on similarities over 12 leads, the Cornell voltage criterion (for men: (R amplitude in lead aVL + S across the expression data. Using these membership probabilities and a set of trait- amplitude in lead V3) × QRS duration; for women: (R amplitude in lead aVL + S associated loci, DEPICT tests whether any of the 14,461 reconstituted gene sets are amplitude in lead V3 + 0.8 mV) × QRS duration) and the Sokolow-Lyon voltage enriched for genes at the trait-associated loci, and prioritises genes that share criterion ((S amplitude in lead V1 + R amplitude in lead V6) × QRS duration). We predicted functions with genes at other trait-associated loci. Additionally, DEPICT also used four phenotypes derived from automated ECG diagnoses: left anterior uses 37,427 human mRNA microarrays to search for tissues/cell types in which fascicular block (LAFB), left bundle branch block (LBBB), left posterior fascicular genes from associated loci are highly expressed (all genes harbouring variants in LD block (LPFB) and right bundle branch block (RBBB). of r2 > 0.5 from the most significant variant). We ran DEPICT using all QRS- We used seven phenotypes describing measurements from echocardiograms: associated variants. We also used DEPICT to compute pairwise Pearson correla- aortic root diameter (N = 19,513), ejection fraction (N = 17,109), left ventricular tions between all reconstituted gene sets and clustered them by similarity using the end-diastolic diameter (N = 18,487), left ventricular end-systolic diameter (N = Affinity Propagation method71. 5,704), mitral regurgitation (N = 5,291), left ventricular posterior wall thickness = = (N 18,626) and interventricular septum thickness (N 18,775). We obtained Reporting summary. Further information on research design is available in these measurements from a database of 53,122 echocardiograms from 27,460 the Nature Research Reporting Summary linked to this article. individuals, read by cardiologists at LUH between 1994 and 2015. deCODE genetics has extensive medical information on cardiovascular diseases for use in association analyses. The QRS variants were tested for association with Data availability 22 cardiovascular diseases: aortic valve stenosis (N = 2457), atrial fibrillation (N = The Icelandic population WGS data has been deposited at the European Variant Archive 14,710), coarctation of the aorta (N = 119), complete atrioventricular block (N = under accession code PRJEB8636. The GWAS summary statistics are available at https:// 1008), congenital malformations of cardiac septa (N = 1884), coronary artery www.decode.com/summarydata. The data supporting the findings of this study are disease (N = 38,918), dilated cardiomyopathy (N = 424), heart failure (N = available within the article, its Supplementary Data files and upon request. The UK 15,237), hypertension (N = 44,290), hypertrophic cardiomyopathy (N = 372), Biobank data can be obtained upon application (ukbiobank.ac.uk). ischemic stroke (N = 5626), mitral valve disease (N = 940), myocardial infarction (N = 24,691), pacemaker insertion (N = 3578), patent ductus arteriosus (N = 628), perimyocarditis (N = 971), pre-excitation Wolff-Parkinson-White syndrome (N = Received: 6 June 2019; Accepted: 9 September 2019; 275), sick sinus syndrome (N = 3568), sudden cardiac death (N = 3128), supraventricular tachycardia (N = 1461), tetralogy of fallot (N = 60) and ventricular tachycardia (N = 945). The sample sets were based on discharge diagnoses from LUH from 1987 to 2017. The controls consisted of disease-free References controls (up to 380,000), randomly drawn from the Icelandic genealogical database, 1. Hancock, E. W. Hurst’s the heart. JAMA. 293, 1799–1800 (2005). and individuals from other genetic studies at deCODE. 2. Teodorescu, C. et al. 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66. Lage, K. et al. A human phenome-interactome network of protein complexes Competing interests implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007). Authors afﬁliated with deCODE genetics/Amgen Inc. declare competing interests as 67. Bult, C. J. et al. Mouse genome informatics in a new age of biological inquiry. employees. All other authors declare no competing interests. In Proc. IEEE International Symposium on Bio-Informatics and Biomedical – Engineering, BIBE 2000,29 32 https://doi.org/10.1109/BIBE.2000.889586 Additional information (2000). 68. Croft, D. et al. Reactome: a database of reactions, pathways and biological Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467- processes. Nucleic Acids Res. 39, D691–D697 (2011). 019-12682-9. 69. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Correspondence and requests for materials should be addressed to D.F.G. or K.S. Res. 40, D109–D114. (2012). 70. Raychaudhuri, S. et al. Identifying relationships among genomic disease Peer review information Nature Communications thanks the anonymous reviewers for regions: predicting genes at pathogenic SNP associations and rare deletions. their contribution to the peer review of this work. Peer reviewer reports are available. PLoS Genet. 5, e1000534 (2009). 71. Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Reprints and permission information is available at http://www.nature.com/reprints Science 315, 972–976 (2007). Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations.

Acknowledgements We thank all study subjects for their valuable participation as well as our colleagues who Open Access This article is licensed under a Creative Commons contributed to data collection, sample handling and genotyping. This research was Attribution 4.0 International License, which permits use, sharing, conducted using the UK Biobank Resource under Application Number 24711. adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless Author contributions indicated otherwise in a credit line to the material. If material is not included in the K.N., G.S., R.B.T., V.T., S.R., A.H., S.G., G.T., U.T., D.O.A., P.S., H.H., D.F.G. and K.S. article’s Creative Commons license and your intended use is not permitted by statutory designed the study and interpreted the results. J.D.S, S.S.S, G.O., E.L.S, K.A. and R.D. regulation or exceeds the permitted use, you will need to obtain permission directly from coordinated and managed phenotype data ascertainment and Icelandic subject recruit- the copyright holder. To view a copy of this license, visit http://creativecommons.org/ ment. J.V.S., F.W.A. and V.T. provided summary level statistics for the replication licenses/by/4.0/. cohort. K.N., G.S., D.F.G., O.B.D. and P.S. performed the statistical and bioinformatics analyses. K.N., G.S., R.B.T., P.S., H.H., D.F.G. and K.S drafted the manuscript. All authors contributed to the ﬁnal version of the manuscript. © The Author(s) 2019

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